Efficient probe selection for fault localization using the property of submodularity

As the computer network increasingly grows larger and more complex, fault diagnosis has become a challenging task. Active probing is an efficient tool for fault localization. By implementing some test programs and analyzing the results, active‐probing‐based techniques can perform diagnosis efficiently and adaptively. Because probes may generate additional traffic overhead, it is important to appropriately select small number of probes to reach the desired diagnostic capability. However, the computation of probe selection problem in such environment is extremely expensive. Most of the past works purchase the speed at the cost of diagnostic accuracy. In this paper, we first verify that probe selection problem satisfies the property of submodularity. Then we take the use of the property and develop a submodularity‐based selection algorithm with following novel features: (i) it is cost effective, failure resistant and more accurate; (ii) it could deal with the uncertainties about the network structures and the observations; and (iii) it can select the required probes in near‐linear time. Finally, we implement submodularity‐based selection algorithm and other two representative probe selection algorithms (bounded path enumeration approximation algorithm and greedy search algorithm) on different settings of networks. The results have shown how the new algorithm outperforms the former two algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

一种稀疏表示的二维DOA降维估计新算法

提出了一种基于空间锥角降维的二维DOA稀疏分解估计新方法,解决了利用稀疏表示方法进行二维DOA估计时计算复杂度大的问题.根据L阵列的结构特性,引入空间锥角表示信号的二维DOA信息,构造空间锥角冗余字典,通过稀疏正则化求解实现空间锥角的估计,然后利用求解得到功率实现L阵列中两个子阵之间的空间锥角配对,从而达到对多来波的二维DOA估计的目的,其避免了方位角和俯仰角组合而造成冗余字典庞大的问题,极大地减少了稀疏分解的计算量.仿真和实测数据结果均验证了该方法的有效性和优越性,为进一步的工程应用奠定了基础.     

基于鲸鱼优化算法的贝叶斯分类器研究与应用

朴素贝叶斯分类器的条件独立性这一假设称为“朴素贝叶斯假设”,其限制了朴素贝叶斯分类的适用范围和分类准确率,为了解决问题,本文提出用改进的鲸鱼优化算法来优化朴素贝叶斯分类器,并对算法进行应用研究。改进的鲸鱼优化算法使用禁忌搜索机制来跳出算法寻优时候易陷入局部最优的误区。为了削弱朴素贝叶斯分类器独立性假设,通过改进的鲸鱼优化算法自动搜索分类器的属性全局性权值,从而提升了加权贝叶斯分类器的运算的准确率。试验证明,相比较传统的朴素贝叶斯分类算法,本文优化后的加权贝叶斯分类算法具有更精准的分类结果。最后提出将改进后的属性加权贝叶斯分类器应用到“移动云”建设的方案。     

Trust establishment in cooperative wireless relaying networks

In cooperative wireless networks, relay nodes are employed to improve the performance of the network in terms of throughput and reliability. However, the presence of malicious relay nodes in the network may severely degrade the performance of the system. When a relay node behaves maliciously, there exists a possibility that such a node refuses to cooperate when it is selected for cooperation or deliberately drops the received packets. Trust establishment is a mechanism to detect misbehaving nodes in a network. In this paper, we propose a trust establishment method for cooperative wireless networks by using Bayesian framework. In contrast with the previous schemes proposed in wireless networks, this approach takes the channel state information and the relay selection decisions into account to derive a pure trust value for each relay node. The proposed method can be applied to any cooperative system with a general relay selection policy whose decisions in each cooperative transmission are independent of the previous ones. Moreover, it does not impose additional communication overhead on the system as it uses the available information in relay selection procedure. Copyright © 2012 John Wiley & Sons, Ltd.     

（2，2）贝叶斯理性秘密共享方案

在理性秘密共享协议中,自利性目标可能会驱使理性参与者偏离协议,从而影响协议的公平性。在（t, n）门限理性秘密共享方案中,其特殊情形（2,2）理性秘密共享方案的公平性较难实现。在同时考虑理性参与者的眼前利益和长远利益的基础上,基于不完全信息动态博弈模型,通过分析理性参与者在（2,2）秘密重构阶段可能采取的策略和信念系统,引入理性参与者的期望收益,研究了（2,2）理性秘密共享重构阶段的完美贝叶斯均衡问题。进一步结合机制设计理论中的VCG（Vickrey-Clarke-Groves）机制,设计激励相容的交互记录机制来约束理性参与者的行为,在不需要秘密分发者保持在线的情形下,提出一个适用于异步通信的公平的（2,2）理性秘密共享方案。     

一种改进的基于K-SVD字典的图像去噪算法

为了更好地实现图像的去噪效果,提出了一种改进的基于K-SVD（Singular Value Decomposition）字典学习的图像去噪算法。首先,将输入的含噪信号进行K均值聚类分解,将得到的图像块进行稀疏贝叶斯学习和噪声的更新,当迭代到一定次数时继续使用正交匹配追踪（Orthogonal Matching Pursuit,OMP）算法对图像块进行稀疏编码,然后在完成稀疏编码的基础上通过奇异值分解来逐列更新字典,反复迭代至得到过完备字典以实现稀疏表示,最后对处理过的图像进行重构,得到去噪后的图像。实验结果表明,本文的改进算法相对于传统的K-SVD字典的图像去噪能够在保留图像边缘和细节信息的同时,更有效地去除图像中的噪声,具有更好的视觉效果。     

小斜视角单输入多输出稀疏线性阵列相参频率分集成像

现代高分辨雷达通过大带宽获得高距离分辨率,通过大孔径获得高方位分辨率。该文根据单输入多输出(SIMO)线性阵列天线的基本原理,借鉴了频率分集相参处理的思路,用两个阵元实现了多阵元所形成的波束,并结合高分辨1维距离像(HRRP)方法,实现了目标的2维成像。进而为了减小频率分集所需的发射阵元工作带宽,该文结合稀疏天线阵元思想,通过设计阵元分布,用较少的频率分集实现了大孔径。仿真验证了所提方法的有效性。     

基于稀疏贝叶斯学习的低空测角算法

为解决米波雷达低空测角的精度问题,该文结合稀疏贝叶斯学习方法,利用相邻快拍稀疏结构的相似性,将多观测向量模型通过Kronecker积变换成具有块稀疏结构的单观测向量模型,同时通过矩阵变换解决了贝叶斯准则在复数域中的应用。通过稀疏贝叶斯学习的不断迭代恢复出了信号在感知矩阵下的系数矩阵,得到了信源的角度信息。仿真实验验证了该方法相对于广义MUSIC和M-FOCUSS算法具有更好的性能,并且分析了快拍数变化对算法性能的影响。     

基于压缩感知的加速前向后向匹配追踪算法

前向后向匹配追踪(FBP)算法作为一个新颖的两阶段贪婪逼近算法,因为较高的重构精度和不需要稀疏度作为先验信息的特点,受到了人们的广泛关注。然而,FBP算法必须运行更多的时间才能得到更高的精度。鉴于此,该文提出加速前向后向匹配追踪(AFBP)算法。该算法利用每次迭代中候选支撑集的信息,实现对已删除原子的再次加入,以此减少算法迭代次数。通过不同非零项分布的稀疏信号和稀疏图像的仿真结果表明,相对于FBP算法,该文提出的方案在不降低重构精度的同时,大幅降低了算法运行时间。     

MIMO-OFDM系统的低复杂度递推信道跟踪算法

该文针对时变多径信道下的MIMO-OFDM系统,基于变分贝叶斯原理,提出了一种新的联合信号检测和信道跟踪的低复杂度半盲贝叶斯迭代接收机。针对该接收机,基于递推变分期望最大化(RVBEM)算法,提出了一种RVBEM信道跟踪算法。由于RVBEM算法需要进行矩阵求逆,因此以该算法为基础推导得到了一种时频域联合递推的低复杂度信道跟踪(TF-LCRVBEM)算法。TF-LCRVBEM算法不仅完全避免了矩阵求逆运算,还通过合理的近似使得算法只具有线性复杂度。分析和仿真表明,在时变多径信道下,所提迭代接收机具有远优于传统接收机和接近理想接收机的性能。